Source code for jmetal.problem.multiobjective.zdt

from math import sqrt, pow, sin, pi, cos

from jmetal.core.problem import FloatProblem
from jmetal.core.solution import FloatSolution

"""
.. module:: ZDT
   :platform: Unix, Windows
   :synopsis: ZDT problem family of multi-objective problems.

.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""


[docs]class ZDT1(FloatProblem): """ Problem ZDT1. .. note:: Bi-objective unconstrained problem. The default number of variables is 30. .. note:: Continuous problem having a convex Pareto front """ def __init__(self, number_of_variables: int=30): """ :param number_of_variables: Number of decision variables of the problem. """ super(ZDT1, self).__init__() self.number_of_variables = number_of_variables self.number_of_objectives = 2 self.number_of_constraints = 0 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['x', 'y'] self.lower_bound = self.number_of_variables * [0.0] self.upper_bound = self.number_of_variables * [1.0]
[docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: g = self.eval_g(solution) h = self.eval_h(solution.variables[0], g) solution.objectives[0] = solution.variables[0] solution.objectives[1] = h * g return solution
[docs] def eval_g(self, solution: FloatSolution): g = sum(solution.variables) - solution.variables[0] constant = 9.0 / (solution.number_of_variables - 1) return constant * g + 1.0
[docs] def eval_h(self, f: float, g: float) -> float: return 1.0 - sqrt(f / g)
[docs] def get_name(self): return 'ZDT1'
[docs]class ZDT2(ZDT1): """ Problem ZDT2. .. note:: Bi-objective unconstrained problem. The default number of variables is 30. .. note:: Continuous problem having a non-convex Pareto front """
[docs] def eval_h(self, f: float, g: float) -> float: return 1.0 - pow(f / g, 2.0)
[docs] def get_name(self): return 'ZDT2'
[docs]class ZDT3(ZDT1): """ Problem ZDT3. .. note:: Bi-objective unconstrained problem. The default number of variables is 30. .. note:: Continuous problem having a partitioned Pareto front """
[docs] def eval_h(self, f: float, g: float) -> float: return 1.0 - sqrt(f / g) - (f / g) * sin(10.0 * f * pi)
[docs] def get_name(self): return 'ZDT3'
[docs]class ZDT4(ZDT1): """ Problem ZDT4. .. note:: Bi-objective unconstrained problem. The default number of variables is 10. .. note:: Continuous multi-modal problem having a convex Pareto front """ def __init__(self, number_of_variables: int=10): """ :param number_of_variables: Number of decision variables of the problem. """ super(ZDT4, self).__init__(number_of_variables=number_of_variables) self.lower_bound = self.number_of_variables * [-5.0] self.upper_bound = self.number_of_variables * [5.0] self.lower_bound[0] = 0.0 self.upper_bound[0] = 1.0
[docs] def eval_g(self, solution: FloatSolution): g = 0.0 for i in range(1, solution.number_of_variables): g += pow(solution.variables[i], 2.0) - 10.0 * cos(4.0 * pi * solution.variables[i]) g += 1.0 + 10.0 * (solution.number_of_variables - 1) return g
[docs] def eval_h(self, f: float, g: float) -> float: return 1.0 - sqrt(f / g)
[docs] def get_name(self): return 'ZDT4'
[docs]class ZDT6(ZDT1): """ Problem ZDT6. .. note:: Bi-objective unconstrained problem. The default number of variables is 10. .. note:: Continuous problem having a non-convex Pareto front """ def __init__(self, number_of_variables: int=10): """ :param number_of_variables: Number of decision variables of the problem. """ super(ZDT6, self).__init__(number_of_variables=number_of_variables)
[docs] def eval_g(self, solution: FloatSolution): g = sum(solution.variables) - solution.variables[0] g = g / (solution.number_of_variables - 1) g = pow(g, 0.25) g = 9.0 * g g = 1.0 + g return g
[docs] def eval_h(self, f: float, g: float) -> float: return 1.0 - pow(f / g, 2.0)
[docs] def get_name(self): return 'ZDT6'